Regular expressions - Revision history

WikiSysop: /* Exercises to be handed in */

2025-10-03T13:59:33Z

Exercises to be handed in

← Older revision		Revision as of 15:59, 3 October 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Exercise 6 to 8 has strong taste of something I would do at an exam. It is also an interesting beginning of the making a HIV vaccine. The data is real and the methods are real.		Exercise 6 to 8 has strong taste of something I would do at an exam. It is also an interesting beginning of the making a HIV vaccine. The data is real and the methods are real.
	# Make a program that accepts a string as input from the keyboard. Use regular expressions (RE) to determine if the input is a number. The goal is to do this with a SINGLE regex.<br> These should all be considered as numbers: "4" "-7" "0.656" "-67.35555"<br> These are not numbers: "5." "56F" ".32" "-.04" "1+1"<br> Note: The program is very simple, but it is likely the most difficult regular expression, you will have to make in this set of exercises. Perhaps you should do the following exercises before attempting this one - just to get some experience first.
	# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.		# Make a program that accepts a string as input from the keyboard. Use regular expressions (RE) to determine if the input is a number. The goal is to do this with a SINGLE regex.<br> These should all be considered as numbers: "4" "-7" "0.656" "-67.35555"<br> These are not numbers: "5." "56F" ".32" "-.04" "1+1"<br> Note: The program is very simple, but it is likely the most difficult regular expression, you will have to make in this set of exercises. Perhaps you should do the following exercises before attempting this one - just to get some experience first.<br><br>
	# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.		# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.<br><br>
	# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contains 3 entries that should be discarded (V00179, J00265, J02989).		# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.<br><br>
	# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br>Consensus: MALWMRLLPLLALLALWEPDPAGAFVNGHLCGSHLVEALYLVCGERGFFYTPKSRREVEDPGVGGLELGGGP<br>		# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contains 3 entries that should be discarded (V00179, J00265, J02989).<br><br>
	# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.		# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br>Consensus: MALWMRLLPLLALLALWEPDPAGAFVNGHLCGSHLVEALYLVCGERGFFYTPKSRREVEDPGVGGLELGGGP<br><br><br>
	# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.		# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.<br><br>
			# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.<br><br>
	# You must prepare the epitopes in ''HIVepitopes.txt'' for machine learning. A common ML problem is when you have many data points (here epitopes) that look like each other. This introduces an unwanted bias in the ML predictions. Your job is to eliminate epitopes that are too similar using the Hobohm-1 algorithm. Hobohm-1 works like this: Look a the first epitope in the list. Compare it sequentially with the rest of the epitopes. If an epitope is too similar with the first, throw it away. Now no epitopes looks like the first. Proceed to the second epitope and repeat the comparing and possibly throwing away of the subsequent epitopes. Proceed to the third epitope in the list. Repeat this pattern until you have reached the end of the list. Now all epitope left are dissimilar to each other.<br>How to determine if two epitopes are too similar? Easy - you earlier learned about the Hamming distance. Just compute the Hamming distance between two epitopes and if the distance is 3 or less, they are too similar. Save the "surviving" epitopes in the file ''HIVepitopesML.txt''.<br>Hint: Think about the Hobohm-1 algorithm before you implement it. You can easily run into problems.<br>You should see a reduction from 15909 epitopes to 3842 in 30-60 seconds.		# You must prepare the epitopes in ''HIVepitopes.txt'' for machine learning. A common ML problem is when you have many data points (here epitopes) that look like each other. This introduces an unwanted bias in the ML predictions. Your job is to eliminate epitopes that are too similar using the Hobohm-1 algorithm. Hobohm-1 works like this: Look a the first epitope in the list. Compare it sequentially with the rest of the epitopes. If an epitope is too similar with the first, throw it away. Now no epitopes looks like the first. Proceed to the second epitope and repeat the comparing and possibly throwing away of the subsequent epitopes. Proceed to the third epitope in the list. Repeat this pattern until you have reached the end of the list. Now all epitope left are dissimilar to each other.<br>How to determine if two epitopes are too similar? Easy - you earlier learned about the Hamming distance. Just compute the Hamming distance between two epitopes and if the distance is 3 or less, they are too similar. Save the "surviving" epitopes in the file ''HIVepitopesML.txt''.<br>Hint: Think about the Hobohm-1 algorithm before you implement it. You can easily run into problems.<br>You should see a reduction from 15909 epitopes to 3842 in 30-60 seconds.

	== Exercises for extra practice ==		== Exercises for extra practice ==

WikiSysop: /* Exercises to be handed in */

2025-09-06T19:08:57Z

Exercises to be handed in

← Older revision		Revision as of 21:08, 6 September 2025
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	# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.		# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.
	# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.		# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.
	# You must prepare the epitopes in ''HIVepitopes.txt'' for machine learning. A common ML problem is when you have many data points (here epitopes) that look like each other. This introduces an unwanted bias in the ML predictions. Your job is to eliminate epitopes that are too similar using the Hobohm-1 algorithm. Hobohm-1 works like this: Look a the first epitope in the list. Compare it sequentially with the rest of the epitopes. If an epitope is too similar with the first, throw it away. Now no epitopes looks like the first. Proceed to the second epitope and repeat the comparing and possibly throwing away of the subsequent epitopes. Proceed to the third epitope in the list. Repeat this pattern until you have reached the end of the list. Now all epitope left are dissimilar to each other.<br>How to determine if two epitopes are too similar? Easy - you earlier learned about the Hamming distance. Just compute the Hamming distance between two epitopes and if the distance is 3 or less, they are too similar. Save the "surviving" epitopes in the file ''HIVepitopesML.txt''.<br>Hint: Think about the Hobohm-1 algorithm before you implement it. You can easily run into problems.<br>		# You must prepare the epitopes in ''HIVepitopes.txt'' for machine learning. A common ML problem is when you have many data points (here epitopes) that look like each other. This introduces an unwanted bias in the ML predictions. Your job is to eliminate epitopes that are too similar using the Hobohm-1 algorithm. Hobohm-1 works like this: Look a the first epitope in the list. Compare it sequentially with the rest of the epitopes. If an epitope is too similar with the first, throw it away. Now no epitopes looks like the first. Proceed to the second epitope and repeat the comparing and possibly throwing away of the subsequent epitopes. Proceed to the third epitope in the list. Repeat this pattern until you have reached the end of the list. Now all epitope left are dissimilar to each other.<br>How to determine if two epitopes are too similar? Easy - you earlier learned about the Hamming distance. Just compute the Hamming distance between two epitopes and if the distance is 3 or less, they are too similar. Save the "surviving" epitopes in the file ''HIVepitopesML.txt''.<br>Hint: Think about the Hobohm-1 algorithm before you implement it. You can easily run into problems.<br>You should see a reduction from 15909 epitopes to 3842 in 30-60 seconds.
	You should see a reduction from 15909 epitopes to 3842 in 30-60 seconds.

	== Exercises for extra practice ==		== Exercises for extra practice ==

WikiSysop: /* Exercises to be handed in */

2025-09-06T19:08:41Z

Exercises to be handed in

← Older revision		Revision as of 21:08, 6 September 2025
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	# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.		# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.
	# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.		# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.
	# You must prepare the epitopes in ''HIVepitopes.txt'' for machine learning. A common ML problem is when you have many data points (here epitopes) that look like each other. This introduces an unwanted bias in the ML predictions. Your job is to eliminate epitopes that are too similar using the Hobohm-1 algorithm. Hobohm-1 works like this: Look a the first epitope in the list. Compare it sequentially with the rest of the epitopes. If an epitope is too similar with the first, throw it away. Now no epitopes looks like the first. Proceed to the second epitope and repeat the comparing and possibly throwing away of the subsequent epitopes. Proceed to the third epitope in the list. Repeat this pattern until you have reached the end of the list. Now all epitope left are dissimilar to each other.<br>How to determine if two epitopes are too similar? Easy - you earlier learned about the Hamming distance. Just compute the Hamming distance between two epitopes and if the distance is 3 or less, they are too similar. Save the "surviving" epitopes in the file ''HIVepitopesML.txt''.<br>Hint: Think about the Hobohm-1 algorithm before you implement it. You can easily run into problems.		# You must prepare the epitopes in ''HIVepitopes.txt'' for machine learning. A common ML problem is when you have many data points (here epitopes) that look like each other. This introduces an unwanted bias in the ML predictions. Your job is to eliminate epitopes that are too similar using the Hobohm-1 algorithm. Hobohm-1 works like this: Look a the first epitope in the list. Compare it sequentially with the rest of the epitopes. If an epitope is too similar with the first, throw it away. Now no epitopes looks like the first. Proceed to the second epitope and repeat the comparing and possibly throwing away of the subsequent epitopes. Proceed to the third epitope in the list. Repeat this pattern until you have reached the end of the list. Now all epitope left are dissimilar to each other.<br>How to determine if two epitopes are too similar? Easy - you earlier learned about the Hamming distance. Just compute the Hamming distance between two epitopes and if the distance is 3 or less, they are too similar. Save the "surviving" epitopes in the file ''HIVepitopesML.txt''.<br>Hint: Think about the Hobohm-1 algorithm before you implement it. You can easily run into problems.<br>
			You should see a reduction from 15909 epitopes to 3842 in 30-60 seconds.

	== Exercises for extra practice ==		== Exercises for extra practice ==

WikiSysop: /* Exercises to be handed in */

2025-09-06T18:06:19Z

Exercises to be handed in

← Older revision		Revision as of 20:06, 6 September 2025
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	# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.		# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.
	# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contains 3 entries that should be discarded (V00179, J00265, J02989).		# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contains 3 entries that should be discarded (V00179, J00265, J02989).
	# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br><br>		# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br>Consensus: MALWMRLLPLLALLALWEPDPAGAFVNGHLCGSHLVEALYLVCGERGFFYTPKSRREVEDPGVGGLELGGGP<br>
	# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.		# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.
	# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.		# Continuing the investigation in HIV. Read the ''HIVenv.fsa'' fasta file and create a single set consisting of all possible epitopes for all sequences. Save the epitopes in the file ''HIVepitopes.txt'' - one epitope per line. An epitope is simply a k-mer 9 residues long, which can possibly elicit a immune system response. So save all unique 9-mers in the sequences in the file. To generate a file that you can verify against my file, you must sort the epitopes alphabetically before saving them.

WikiSysop: /* Exercises to be handed in */

2025-09-06T17:46:36Z

Exercises to be handed in

← Older revision		Revision as of 19:46, 6 September 2025
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	# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.		# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.
	# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.		# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.
	# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contains 3 entries that should be discarded.		# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contains 3 entries that should be discarded (V00179, J00265, J02989).
	# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br><br>		# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br><br>
	# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.		# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.

WikiSysop: /* Exercises to be handed in */

2025-09-06T17:06:32Z

Exercises to be handed in

← Older revision		Revision as of 19:06, 6 September 2025
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	# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.		# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.
	# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.		# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.
	# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which ~~contain~~ 3 entries that should be discarded.		# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contains 3 entries that should be discarded.
	# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br><br>		# [[File:consensus.png\|50px\|right]] In the file ''alignment.fsa'' is a protein alignment of part of the insulin gene from different organisms. Read the fasta file and determine the consensus sequence of the alignment, which you print. The consensus sequence is simply the most frequent occurring amino acid on each position.<br><br>
	# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.		# All HIV envelope proteins from various HIV strains in SwissProt have been identified and collected in the file ''HIVenvelope.txt''. Using regular repressions you must extract the ID and the protein sequence from each entry and save them in a fasta file named ''HIVenv.fsa''. This job is not new to you and you can use your '''fastawrite()''' function if you want to.

WikiSysop: /* Subjects covered */

2025-09-06T15:58:32Z

Subjects covered

← Older revision		Revision as of 17:58, 6 September 2025
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	== Subjects covered ==		== Subjects covered ==
	* Regular expressions, duh.		* Regular expressions, duh.
	* Patterns, how to design and use them.		* Patterns, how to design and use them - and not use them.

	== Exercises to be handed in ==		== Exercises to be handed in ==

WikiSysop: /* Exercises to be handed in */

2025-09-05T11:26:03Z

Exercises to be handed in

← Older revision		Revision as of 13:26, 5 September 2025
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	== Exercises to be handed in ==		== Exercises to be handed in ==
	Exercise 5 to 7 has strong taste of something I would do at an exam. It is also an interesting beginning of the making a HIV vaccine. The data is real and the methods are real.		Exercise 6 to 8 has strong taste of something I would do at an exam. It is also an interesting beginning of the making a HIV vaccine. The data is real and the methods are real.
	# Make a program that accepts a string as input from the keyboard. Use regular expressions (RE) to determine if the input is a number. The goal is to do this with a SINGLE regex.<br> These should all be considered as numbers: "4" "-7" "0.656" "-67.35555"<br> These are not numbers: "5." "56F" ".32" "-.04" "1+1"<br> Note: The program is very simple, but it is likely the most difficult regular expression, you will have to make in this set of exercises. Perhaps you should do the following exercises before attempting this one - just to get some experience first.		# Make a program that accepts a string as input from the keyboard. Use regular expressions (RE) to determine if the input is a number. The goal is to do this with a SINGLE regex.<br> These should all be considered as numbers: "4" "-7" "0.656" "-67.35555"<br> These are not numbers: "5." "56F" ".32" "-.04" "1+1"<br> Note: The program is very simple, but it is likely the most difficult regular expression, you will have to make in this set of exercises. Perhaps you should do the following exercises before attempting this one - just to get some experience first.
	# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.		# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.

WikiSysop: /* Exercises to be handed in */

2025-09-05T11:07:50Z

Exercises to be handed in

← Older revision		Revision as of 13:07, 5 September 2025
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	Exercise 5 to 7 has strong taste of something I would do at an exam. It is also an interesting beginning of the making a HIV vaccine. The data is real and the methods are real.		Exercise 5 to 7 has strong taste of something I would do at an exam. It is also an interesting beginning of the making a HIV vaccine. The data is real and the methods are real.
	# Make a program that accepts a string as input from the keyboard. Use regular expressions (RE) to determine if the input is a number. The goal is to do this with a SINGLE regex.<br> These should all be considered as numbers: "4" "-7" "0.656" "-67.35555"<br> These are not numbers: "5." "56F" ".32" "-.04" "1+1"<br> Note: The program is very simple, but it is likely the most difficult regular expression, you will have to make in this set of exercises. Perhaps you should do the following exercises before attempting this one - just to get some experience first.		# Make a program that accepts a string as input from the keyboard. Use regular expressions (RE) to determine if the input is a number. The goal is to do this with a SINGLE regex.<br> These should all be considered as numbers: "4" "-7" "0.656" "-67.35555"<br> These are not numbers: "5." "56F" ".32" "-.04" "1+1"<br> Note: The program is very simple, but it is likely the most difficult regular expression, you will have to make in this set of exercises. Perhaps you should do the following exercises before attempting this one - just to get some experience first.
			# Regex is often used for validation. This time make a small program that ask for an email address, and checks if it is in a proper format. Quite similar to the previous exercise, just a different thing to validate.
	# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.		# Make a program that can read and verify a fasta file. Use your previous function '''fastaread()''' Test the program with ''dna7.fsa'' and ''dnanoise.fsa''. Verification here means that the program prints "DNA fasta" or "Protein fasta" if the file is successfully verified for either dna or protein sequence, and "Not fasta" if unsuccessfully verified. You can find a description of fasta format in [[Biological knowledge needed in the course]]. You are expected to know which symbols are used for DNA and protein sequence - or that you are able to look it up.
	# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contain 3 entries that should be discarded.		# Building on your experience with the previous exercise, make a program that reads a fasta file, discard entries that can not conform to DNA or protein sequence, and rewrite (using your '''fastawrite()''' function) the acceptable entries in the output file ''fastaout.fsa'', in such a way that the normal 60 chars per line is followed with no spaces in between. The program must inform the user how many entries was kept and how many discarded. Hint: Test on ''dnanoise.fsa'', which contain 3 entries that should be discarded.

WikiSysop: /* Exercises for extra practice */

2025-09-05T10:43:48Z

Exercises for extra practice

← Older revision		Revision as of 12:43, 5 September 2025
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	== Exercises for extra practice ==		== Exercises for extra practice ==
	~~protein alignment motif~~